FindPFΔS: Non-Target Screening for PFAS─Comprehensive Data Mining for MS2 Fragment Mass Differences.

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We developed and optimized a vendor-independent open-source Python-based algorithm (FindPFΔS = FindPolyFluoroDeltas) to search for distinct fragment mass differences in MS/MS raw data (.ms2-files). Optimization with PFAS standards, two pre-characterized paper and soil samples (iterative data-dependent acquisition), revealed Δ(CF2)n, ΔHF, ΔCnH3F2n-3, ΔCnH2F2n-4, ΔCnHF2n-5, ΔCnF2nSO3, ΔCF3, and ΔCF2O as relevant and selective fragment differences depending on applied collision energies. In a PFAS standard mix, 94% (36 of 38 compounds from 10 compound classes) could be found by FindPFΔS. The use of fragment differences was applicable to a wide range of PFAS classes and appears as a promising new approach for PFAS identification. The influence of mass tolerance and intensity threshold on the identification efficiency and on the detection of false positives was systematically evaluated with the use of selected HR-MS2-spectra (20,998) from MassBank. To this end, with the use of FindPFΔS, we could identify different unknown PFAS homologues in the paper extracts. FindPFΔS is freely available as both Python source code on GitHub (https://github.com/JonZwe/FindPFAS) and as an executable windows application (https://doi.org/10.5281/zenodo.6797353) with a graphical user interface on Zenodo.", "claims": [{"id": "claim-method", "statement": "Furthermore, to also consider intensity, a number weighted by intensity (Niw) was determined by assigning the lower intensity of two fragments x and y to their difference and summing up the intensities of all identical fragment differences (within a mass tolerance).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35866933", "locator": "sections[6] \"Retrospective Analysis\" paragraph 3", "quote": "Furthermore, to also consider intensity, a number weighted by intensity (Niw) was determined by assigning the lower intensity of two fragments x and y to their difference and summing up the intensities of all identical fragment differences (within a mass tolerance)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Only HR-MS2 spectra (QTOF- and FT-MS instruments) with ≥ 2 peaks (prerequisite for mass differences) were included resulting in 20,998 MS2-spectra from 1921 compounds (filtered by unique compound name).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35866933", "locator": "sections[7] \"Application of FindPFΔS to MassBank\" paragraph 2", "quote": "Only HR-MS2 spectra (QTOF- and FT-MS instruments) with ≥ 2 peaks (prerequisite for mass differences) were included resulting in 20,998 MS2-spectra from 1921 compounds (filtered by unique compound name)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In a PFAS standard mix, 94% (36 of 38 compounds from 10 compound classes) could be found by FindPFΔS.", "kind": "quantitative", "target": {"value": 94.0, "unit": "per cent", "tolerance": null, "source": "In a PFAS standard mix, 94% (36 of 38 compounds from 10 compound classes) could be found by FindPFΔS."}, "evidence": [{"asset": "source:pmid:35866933", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In a PFAS standard mix, 94% (36 of 38 compounds from 10 compound classes) could be found by FindPFΔS."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "In a PFAS standard mix, 94% (36 of 38 compounds from 10 compound classes) could be found by FindPFΔS.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35866933"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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